Method of capacitive measurement by non-regular electrodes, and apparatus implementing such a method

ABSTRACT

The invention relates to a method of absolute capacitive measurement of an object with respect to at least two independent electrodes integrated into a man-machine interface device for detecting said object. This method comprises the following steps: a) for each electrode, a value of absolute capacitance between the electrode and the object is measured, a′) a prediction is made by applying a multi-variable nonlinear prediction model to the actual values of absolute capacitance so as to obtain an image of probability densities, these probability densities being considered to be actual corrected values that are used for detecting said object. For example, the multi-variable nonlinear prediction module may be obtained by nonlinear regression on the basis: —of actual values of absolute capacitance that are obtained for a plurality of object positions with respect to said at least two electrodes, and —of an image of probability densities that is obtained for a plurality of object positions with respect to idealized electrodes.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/347,070, filed Mar. 25, 2014 and published on Dec. 25, 2014 as U.S.Publication No. 2014-0379287, which is a national stage entry ofPCT/EP2013/054729, filed Mar. 8, 2013 and published on Sep. 19, 2013 asWO2013/135575, the contents of which are incorporated by referenceherein in their entirety for all purposes.

The present invention relates to a method of measurement of absolutecapacitance (self-capacitance) of an object in the proximity of aplurality of independent electrodes, these electrodes having non-regularsurfaces. It also relates to an electronic gestural interface deviceimplementing the method.

The field of the invention is more particularly, but non-limitatively,that of touch-sensitive and 3D capacitive surfaces used forhuman-machine interface commands.

Communication and work devices are increasingly using a touch-sensitivecontrol interface such as a pad or a screen. It is possible for exampleto mention mobile telephones, smartphones, computers withtouch-sensitive screens, pads, PCs, mouse devices, touch pads,widescreens, etc.

A large number of these interfaces use capacitive technologies.

The touch-sensitive surface is equipped with conductive electrodesconnected to electronic means which make it possible to measure thevariation of the capacitances appearing between electrodes and theobject to be detected in order to give a command.

The capacitive techniques currently used in touch-sensitive interfacesmost often use two layers of conductive electrodes in the form of rowsand columns. The most widely used geometric topology is that each rowand each column is composed of rhombuses which are connected together inthe vertical direction to form a column and in the horizontal directionto form a row.

Two operating modes can be produced using this geometric topology fordetecting the presence of an object in front of the surface:

1. The electronics measure the coupling capacitances which exist betweenthese rows and columns. When a finger is very close to the activesurface, the coupling capacitances in the proximity of the finger aremodified and the electronics can thus locate the position in 2D (XY), inthe plane of the active surface. These technologies make it possible todetect the presence and the position of the finger through a dielectric.They have the advantage of allowing a very good resolution in thelocation of one or more fingers in the plane (XY) of the sensitivesurface. These techniques have however the drawback of generating inprinciple large leakage capacitances at the level of the electrodes andof the electronics. These leakage capacitances can moreover drift overtime due to ageing, the deformation of the materials or the effect ofthe variation of the environmental temperature. These variations candegrade the sensitivity of the electrodes, or can even trigger commandsin an untimely manner. Another drawback of this technique is that theelectric field generated between the rows and the columns remainsespecially concentrated around the surface and the change of couplingcapacitance does take place only for objects very close to the surface,or even in contact. This limits this technique to touch and 2D useexclusively.

2. The electronics measure—for each row and each column ofelectrodes—the absolute capacitance which appears between the object andthe electrode in question. The advantage of this method is that theelectric field is radiated further from the surface, making it possibleto measure objects which are located several centimetres above thescreen. The drawback of this method is the limitation of detectingseveral objects because of a positional ambiguity of two objects, infact if the X or Y coordinates of these objects are permuted, thecapacitances measured will be identical. For a person skilled in theart, this phenomenon is known as “ghosting”.

Techniques are also known that make it possible to measure the absolutecapacitance which appears between electrodes and an object to bedetected. For example, document FR 2 844 349 by Rozière is known, whichdiscloses a capacitive proximity detector comprising a plurality ofelectrodes which will be independently excited and measured. Thisdetector makes it possible to measure the absolute capacitance and thedistance between the electrodes and the objects in the proximity.

These techniques make it possible to obtain measurements of capacitancebetween the electrodes and the objects with high resolution and highsensitivity. This makes it possible to detect for example a finger at adistance of several centimetres without ambiguity. The detection can bedone in three-dimensional space (XYZ) but also on a surface, in a plane(XY). These techniques offer the possibility of developing trulycontactless gestural interfaces and also make it possible to improve theperformance of touch-sensitive interfaces.

In order to interpret the measurements easily, to detect the presence ofan object reliably and to estimate its position accurately, theelectrodes are ideally disposed regularly on the surface, whichpreferably results in disposing the electrodes in a configuration havingidentical rectangular geometry for all of the electrodes. The size ofthe electrodes is approximately identical to or smaller by about 50%than the size of the object to be detected. Typically, an electrodesurface area ranging from 0.35 to 0.65 cm² is well suited to anapplication of the human interface type, where the object to be detectedis a human finger. This type of regular partitioning is well suited tointerfaces of the virtual 2D/3D button type where the electrodes areetched on an electronic printed circuit board (PCB) and are supplied bya power supply which is located below the conductive surface layer.

However, because of the constraints related to the transparency of thesurfaces in certain applications of the “smartphone” type, where thedetection surface must allow the passage of a maximum amount of lightcoming from the display, the surface of the electrodes and itselectrical connection to the electronic excitation and acquisitioncircuit are disposed on the same layer. The electrical connections makeit possible to connect the electrodes located at the centre of thescreen to the periphery of the screen and then these connectionsdescend, if this is necessary, along the circumference of the screen.The connections on the circumference can be protected or not protectedfrom the capacitive interference of the environment by covering themwith an isolating surface and then by disposing a conductor nearby,known as a guard conductor, which is excited with the same electricalpotential as that of the electrodes. If the tracks are “guarded”, theyare considered as non-measuring and are not considered as part of thecapacitive measurements. In the opposite case, these tracks are anintegral part of the measurement. The connections which connect thecentral electrodes to the periphery result in each individualmeasurement no longer being localized in a rectangular surface. Itmeasures a response not only when the object is above the principalsurface, but as soon as the object is in the proximity of the connectiontrack which can be at a distance from the principal measuring surface.

However, the constraint of better transparency of the screen makes itnecessary to dispose the electrodes and their connections on the samesurface. This makes it possible to reduce the manufacturing cost. Thissimplification makes it possible to have great reliability byeliminating the inter-layer connection elements.

The objective of the present invention is a new measuring methodlimiting the interference due to the connection tracks.

Another objective of the invention is to reduce the cost of the designof a device comprising a gestural human-machine interface.

At least one of the objectives is achieved with a method of measuringthe absolute capacitance of an object with respect to at least twoindependent electrodes integrated in a human-machine interface devicefor the detection of said object. According to the invention, thismethod comprises the following steps:

a) for each electrode, a value of absolute capacitance between theelectrode and the object is measured,

a′) a prediction is made by applying a multi-variable nonlinearprediction model to the actual values of absolute capacitance so as toobtain an image of probability densities (at the time of themeasurement, in real time) these probability densities being consideredas corrected absolute capacitance values that are used for the detectionof said object.

With such a method according to the invention, a multi-variablenonlinear prediction model is used for correcting the actual absolutecapacitance values. This correction makes it possible to transform theseactual values into corrected values making it possible to compensate forvarious defects in the design of the electrodes. These defects can bedue to a non-optimized geometric shape of the electrodes which wouldlimit in particular the resolution of the detection. For example, thisgeometric shape can be due to connection tracks which leave from theperiphery of a plane of electrodes and go to electrodes disposed in thecentral area of the plane of electrodes. This unfavourable geometricshape can be described as a non-regular surface. By “non-regularsurface” is meant a surface which does not have a regular geometricshape such as a square, a rectangle, a circle or any other shape. By wayof example, such a surface is a surface which comprises a rectangleadjoining a thin strip such as a connection track.

As will be seen below, the image of probability densities can be animage of virtual values obtained by measurement on an ideal plane ofelectrodes or an image of functions obtained from a Gaussiandistribution for an ideal plane of electrodes. This ideal plane ofelectrodes can be a theoretical design for a high-resolution arrangementof the electrodes, with a number and shape of the electrodes differentfrom the number and shape of the actual electrodes.

Preferably, the multi-variable nonlinear prediction model is obtained bynonlinear regression on the basis:

of actual values of absolute capacitance obtained for a plurality ofobject positions with respect to said at least two electrodes, and

of an image of probability densities obtained for a plurality of objectpositions with respect to idealized electrodes.

The determination by nonlinear regression can be obtained by anartificial neural networks model. Different ways of using neuralnetworks exist and are known to a person skilled in the art.

Preferably, the present invention provides for the followingimplementation: in step a) a vector V_(raw) is constituted from themeasured absolute capacitance values and the prediction in step a′)comprises the following steps:

b) application of a first nonlinear transformation F2 to at least thevector V_(raw) in order to obtain a vector X2,

c) application of an affine transformation in order to obtain a vectorY2 by multiplying the vector X2 by a matrix M2 and adding a translationvector Y02; the matrix M2 being a matrix of transfer between a vector ofactual values of absolute capacitance obtained on electrodes ofnon-regular surface in the presence of an object and a vector of virtualvalues obtained for idealized electrodes in the presence of an object,

d) application to at least the vector Y2 of a second nonlineartransformation which is inverse to the first nonlinear transformation F2in order to obtain a correction vector V_corr, and

e) use of the correction vector V_corr as values of absolute capacitancefor the detection of said object.

With this implementation, a correction of the measured values ofabsolute capacitance is carried out. These values have been modified inorder to eliminate in particular the influence of the connection tracks,these connection tracks being the difference between the actualelectrodes and the virtual electrodes. It is by means of the matrix M2and the translation vector YO2 that the model is used between the actualelectrodes and the virtual electrodes considered as ideal.

The object which moves in a volume in front of or close to theelectrodes can be detected accurately thanks to the method according tothe invention. This makes it possible to easily envisage the design of amatrix array of electrodes disposed on the same layer as theconnections.

According to an advantageous feature of the invention, differentfunctions F2 can be used, such as for example:

-   -   F2(V_(raw))=1/V_(raw)    -   F2(V_(raw))=1/(V_(raw)/Vmax+β), Vmax being a predetermined        maximum voltage, and β being a positive number; or    -   F²(V_(raw))=V_(raw)/Vmax, Vmax being a predetermined maximum        voltage.

According to an advantageous embodiment of the invention, after step a),the following steps are carried out:

-   -   filtering of the values of the vector V_(raw) in order to obtain        a vector V_(inf) _(_) _(raw)    -   application of a nonlinear transformation F1 to the vector        V_(inf) _(_) _(raw) in order to obtain a vector X1,    -   application of an affine transformation in order to obtain a        vector Y1 by multiplying the vector X1 by a matrix M1 and by        adding a translation vector Y01; the matrix M1 being a matrix of        transfer between a vector of actual values of absolute        capacitance obtained on electrodes of non-regular surface in the        absence of an object of detection, and a vector of virtual        values in the absence of an object of detection, and    -   application to the vector Y1 of a nonlinear transformation which        is inverse to the nonlinear transformation F1 in order to obtain        a correction vector Vinf_corr,    -   then carrying out steps b) to e) in which, in step b), the        nonlinear transformation F2 is applied to the vectors V_(raw)        and V_(inf) _(_) _(raw) in order to obtain a vector X2 that is a        function of V_(raw) and V_(inf) _(_) _(raw); and in step d) a        second nonlinear transformation, which is inverse to the first        nonlinear transformation F2, is applied to the vectors Y2 and        Vinf_corr in order to obtain a correction vector V_corr which is        a function of Y2 and Vinf_corr.

With such an embodiment, there is firstly corrected a set of valuesrelating to the actual electrodes without taking account of the objectof interest, it being the filtering that makes it possible to eliminatethe influence of the object of interest. The second correction uses theresults of the first correction to correct the values relating to theactual electrodes taking the object of interest into account.

According to the invention, the function F1 can also have differentforms such as:

-   -   F1(V_(raw))=1/V_(raw).    -   F1(V_(raw))=1/(V_(raw)/Vmax+β), Vmax being a predetermined        maximum voltage, and β being a positive number, or    -   F1(V_(raw))=V_(raw)/Vmax, Vmax being a predetermined maximum        voltage.

In this embodiment, the function F2 can be such that:

-   -   F2(V_(raw), V_(inf*))=V_(raw)/V_(inf*); V_(inf*) being equal to        V_(inf) _(_) _(raw) during the nonlinear transformation in step        b), and equal to Vinf_corr during the inverse nonlinear        transformation in step d), or    -   F2(V_(raw), V_(inf*))=1−(V_(raw)/V_(inf*)); V_(inf*) being equal        to V_(inf) _(_) _(raw) during the nonlinear transformation in        step b), and equal to Vinf_corr during the inverse nonlinear        transformation in step d).

During step e), a step of normalization of the correction vector V_corrcan also be envisaged, during which the following steps are carried out:

-   -   filtering the correction vector V_corr in order to obtain a        filtered vector V_corr_f, and    -   normalizing the correction vector with the filtered vector        V_corr_f in order to obtain a normalized vector V_corr_nor.

According to an advantageous feature of the invention, the filtering isobtained according to one of the following formulae:

V(t0)=max {V(t): t∈(−∞, t0)}; t0 being the time of the measurement, V(t)being the vector to which the filtering is applied, t being the timeindex, or

V(t0)=max {V(t): t∈(t0−windowsize, t0)}, where windowsize is a timeperiod of the auto-calibration window—i.e. the time period in which anyunchanged interference will be considered as caused by an object of nointerest—t0 being the time of the measurement, V(t) being the vector towhich the filtering is applied, t being the time index.

The filtering can also be obtained simply by replacing the vector towhich the filtering is applied by a predetermined vector.

According to the invention, the matrix M can be obtained by the methodof partial least squares, the vector of virtual values being a vector ofvalues obtained for idealized electrodes. These are regular electrodeshaving no connection track at a distance.

In another way, provision can be made for obtaining the matrix M from asampling of a probability density function resulting from a multitude ofexact object positions with respect to the electrodes, the vector ofvirtual values being a vector the values of which are probabilities ofpresence. In this case, the probability density function canadvantageously be a 2D Gaussian distribution centred on each horizontalobject position, the width of which depends on the vertical position ofthe object, this Gaussian distribution being defined by the followingformula:

Gj(t)=A(z0)*exp[−((xj−xo)²+(yj−yo)²)/σ(zo)²],

where: (xj,yj) are coordinates of a regular grid on a detection surfacecomprising the electrodes; (xo(t),yo(t),zo(t)) are 3D coordinates of theend of the object closest to the detection surface; A(zo) and σ(zo) aretwo predetermined functions depending on the distance z0 in a monotonicmanner, A(z) being decreasing and σ(zo) increasing.

According to another aspect of the invention, an electronic device isprovided, comprising:

-   -   two independent electrodes integrated in a human-machine        interface device    -   a processing unit for detecting the position of an object by        measuring the absolute capacitance of said object with respect        to the electrodes. According to the invention, the processing        unit is configured to implement at least one of the steps        described above.

The device can comprise a touch-sensitive screen or not.

In general, the detection can be a two-dimensional detection on a screenor a gestural detection in a three-dimensional volume in the proximityof a screen or not (for example a detection pad disposed behind a woodenpanel, etc.).

An embodiment of the electronics of the present invention can be the onedescribed by patent WO 2011/015795 A1. In this embodiment, an activeguard has been positioned in order to minimize the capacitive leakageand to provide better quality of measurement of objects of interest. Ifthe active guard is not provided, the capacitive leakage should becalibrated and deducted.

The electrodes are preferably designed on the basis of tin-doped indiumoxide (ITO). Other materials transparent to light such asaluminium-doped zinc oxide (AZO) or tin-doped cadmium oxide can also beused.

Other advantages and characteristics of the invention will becomeapparent on examination of the detailed description of an embodimentwhich is in no way limitative, and the attached diagrams, in which:

FIGS. 1a and 1b are diagrammatic views of a device according to theinvention;

FIG. 2 is a diagrammatic view illustrating the variation of theelectrical potential as a function of the movement of an object ofinterest above a device according to the invention;

FIGS. 3a and 3b are diagrammatic views illustrating, on the one hand,actual electrodes with a non-regular division and, on the other hand,virtual electrodes with a regular division of the surface;

FIG. 4 is a diagrammatic view illustrating circular virtual electrodes;

FIGS. 5a and 5b are diagrammatic views illustrating, on the one hand,actual electrodes with a non-regular division and, on the other hand,virtual electrodes with a regular division of the surface but with ahigher concentration of electrodes towards the edges;

FIG. 6 is a diagrammatic view illustrating a device of the “smartphone”type according to the invention;

FIGS. 7a and 7b are respectively a diagrammatic view illustratingnumbered actual electrodes and a diagrammatic zoom on one of theelectrodes;

FIG. 8 is a diagrammatic view illustrating a flowchart of stepsaccording to the invention; and

FIG. 9 is a diagrammatic view of the general method according to theinvention.

In general, in FIGS. 1a and 1b , a device AP according to the inventioncan be seen. It can be a telephone of the “smartphone” type or a digitaltablet provided with a touch-sensitive screen. This device AP comprisesa detection surface SD which is the touch-sensitive part under which inparticular a plane (flat or curved) of electrodes is located. Thisdetection surface SD comprises, starting from its top part, severallayers made of transparent material such as for example:

-   -   an outer window VE,    -   an anti-debris film FAD,    -   a transparent adhesive CT, and    -   a polarizer P,    -   electrodes E made of transparent material such as tin-doped        indium oxide (ITO),    -   a glass support S for electrodes,    -   a guard G which is a layer made of transparent conductive        material such as tin-doped indium oxide (ITO), and    -   a display screen EC which must be visible from the outside        through the outer window VE.

The electrodes and the guard are therefore located under the detectionsurface and are made of a transparent conductive material which has highresistivity.

There can also be seen a non-detecting surface SND which in this casesurrounds the detection surface SD. This surface is generally opaquefrom the outside and has no electrodes but has connection tracks PT andflexible connectors CF which are metallic and therefore have virtuallyzero resistivity.

Even though the invention is not limited thereto a method according tothe invention will now be described in which the regression and theprediction make use of three transformations: a nonlineartransformation, a linear transformation and then a second nonlineartransformation. As previously stated, other related techniques can beused, within the family of neural networks in particular.

The present invention can be used in a first calibration step in orderto determine matrices and translation vectors of an affinetransformation. According to an embodiment, a model is produced betweenactual absolute capacitance values and virtual absolute capacitancevalues. By definition, the absolute capacitance CM) measured by eachelectrode with precision is mathematically proportional to the integralof the charge density on the surface Aj of this electrode, FIG. 3aillustrates such electrodes:

C _(j)(t)=∫(∂Φ/∂n)(t)ds,j=1,2, . . . ,N

where N is the number of actual electrodes, n is the vector normal tothe measuring surface, Φ is the electrical potential at the time t andds is the infinitesimal element of the surface. The normal derivative(∂Φ/∂n) is the surface charge density.

The electrical potential verifies the electrostatic equation:

-   -   ΔΦ=0 in Ω, where Ω is the 3D volume representing the        three-dimensional half-volume where the area of sensitivity is        located,    -   ∂Ω=Γ(t) U slab, Γ(t) is a function of the surface of the object        in motion in front of the device, where the slab is the whole of        the active surface to which the excitation potential is applied.    -   The conditions of radiations at infinity,        -   Φ=0 on Γ(t),    -   Φ=1 on the surface of the slab.

FIG. 2, shows a representation of the function Γ(t) in form of a curveprogressing over a detection surface. This curve represents for examplethe movement of a finger above a screen. The electrical potentialdepends on the shape and the position of the object.

The total capacitive surface is partitioned into N electrodes. The twovalues 0 and 1 Volts are given by way of example. The value 0 is thepotential of the earth, and 1 is the reference value of the excitationpotential. It is the floating excitation value in the case of ameasurement by the capacitive floating bridge technique such asdescribed in document WO2011/015795 the content of which is insertedhere by way of reference.

In parallel with the measurements {Cj(t)} made by a true device havingactual electrodes of non-regular shape, the response by a “virtual”device, which is subjected to the same electrical potential and the sameelectrical field as the real device, is considered. The difference isthat this “virtual” device has a more regular division of the electrodesand therefore a capacitive response in the form of an image thatfaithfully reflects the probability density function that an objectexhibits at a location. The virtual absolute capacitance Cv_(j)(t)measured by each electrode is mathematically proportional to theintegral of the charge density on the surface Bj which is an idealsurface of the electrode, i.e. a surface not having a connection track,FIG. 3b illustrating such electrodes:

Cv _(j)(t)=∫(∂Φ/∂n)(t)ds, j=1,2, . . . ,Nv

where Nv is the number of virtual electrodes, Φ is the same electricalpotential at the time t and ds is the infinitesimal element of surface.

The number of virtual electrodes can be identical to or greater than thenumber of actual electrodes. This in order to increase the detectionresolution of two or more objects that are very close to each other.

To determine the model for passing from the actual to the virtual(=ideal electrodes), it is a matter of calculating {Cv_(j)(t)} from{C_(j)(t)} by digital means comprised in the processing unit. Thefunction of passage from one to the other can be determined in severalways.

1. Firstly, a set of positions of the objects on the detection volume ischosen. This set must be representative in order to cover allenvisageable cases of use of the device sufficiently.

2. The electrical potential over this set of positions is calculated forexample by digital simulations by means of a computer. This provides aset of electrical potentials which covers the cases of use of thedevice.

3. Responses with the actual device {C_(j)(t)} and the virtual device{Cv_(j)(t)} over this set are calculated by integrating the same chargedensity (∂Φ/∂n) over the respective surfaces Aj and Bj.

4. A statistical regression or model identification method makes itpossible to determine the model making it possible to calculate{Cv_(j)(t)} approximately as a function of {C_(j)(t)}.

Steps 2 and 3 above can be carried out by digital simulation or by anexperimental method: a device with non-regular electrodes and anotherdevice with idealized regular electrodes measuring the same positions ofthe object in the set fixed in step 1. These two devices can measure thesame sequence sequentially, or in a mechanically synchronized manner inorder to minimize the measurement errors.

In step 4, it is possible to use a regression method such as the“Partial Least Squares” (PLS) method. The PLS method makes it possibleto link the response of the actual electrodes and of the virtualelectrodes with a linear model, through a 3^(rd) variable which is knownas a latent variable. The PLS method is known as bilinear. Other modelidentification techniques such as the methods of least squares withRidge regularization, or Lasso regression, etc. can be used instead ofthe PLS method.

FIG. 3a shows a representation of the actual electrodes. They all havean access track to one of the two lateral edges. The surfaces of theseelectrodes are not regular. In FIG. 3b , the virtual electrodes exhibita partition of the measuring surface into regular rectangles; it ispossible to envisage that the surfaces of the virtual electrodes haveother shapes, for example overlapping disks of the same surface area.Such an embodiment is shown in FIG. 4. The fact that the shape of eachelectrode is a disk makes it possible to have a more isotopic responsein the plane parallel with the detection surface. It is more practicalto implement the response of such overlapping virtual electrodes bysimulation, rather than experimentally.

In the same way as for the shape, the distribution of the virtualelectrodes can be different from that of the actual electrodes. A higherconcentration can be envisaged on the edges in order to better capturethe shape of the probability density function on the edges of thedetection surface, as can be seen in FIG. 5b ; FIG. 5a being arepresentation of the actual electrodes. Therefore the shape and thedistribution of the virtual electrodes can be different from those ofthe actual electrodes. It can also be envisaged to have a denser gridwhere higher detection accuracy is required, for example in the centreof the detection surface.

According to another embodiment, instead of using the response of thevirtual electrodes Cv_(j)(t) as a desired output response, a set ofimages—which does not necessarily have physical significance—can be usedas a desired output. For example the image can be a sampling of theprobability density function originating directly from the exactposition of the object—for example a centred 2D Gaussian distribution,the horizontal position over the object, the width of the Gaussiandistribution depending on the vertical position of the object.

Gj(t)=A(z0)*exp[−((xj−xo)²+(yj−yo)²)/σ(zo)²],

where: (xj,yj) are coordinates of a regular grid on a detection surfacecomprising the electrodes; (xo(t),yo(t),zo(t)) are 3D coordinates of theend of the object closest to the detection surface; A(zo) and σ(zo) aretwo predetermined functions depending on the distance z0 in a monotonicmanner, A(z) being decreasing and σ(zo) increasing.

According to the invention, the model making it possible to obtain themultiplication matrices and the translation vectors of an affinefunction can be obtained by a transformation that converts themeasurements C_(j)(t) to the outputs Cv_(j)(t)}, or alternatively to theoutputs Gj(t).

The invention also comprises a phase of operation during which a useruses a device provided with a screen and a detection device. Such adevice can be an intelligent telephone of the “smartphone” type shown at1 in FIG. 6. This device 1 comprises a display screen 2 and a capacitivedetection device. The latter comprises a floating bridge electroniccircuit (not shown) as described in document WO2011/015795. Such anelectronic circuit comprises in particular capacitive measuringelectrodes and guard electrodes in order to limit the influence ofparasitic capacitances that are sources of interference. In other words,the device can comprise a conductive plane used as an active guardagainst capacitive leakages, this conductive plane can be of floatingbridge technology as described in document FR 2 844 349 by Rozière orother technologies. The device can also not have a guard. In general, aguard is a conductive plane substantially at the same potential as thatof the measuring electrodes.

In FIG. 6, a matrix array of capacitive measuring electrodes 3 isdisposed over a display screen 2. The capacitive electrodes 3 are madeof tin-doped indium oxide (ITO).

FIG. 7a shows the matrix array of the capacitive electrodes 3 which arenumbered from 1 to 60. Each electrode is provided for being connected toan electronic circuit (not shown) from the left or right side peripheralof the device. As seen in FIG. 7b , each capacitive electrode comprisesa working part 6, typically of rectangular shape, and a connection track7 making it possible to connect the working part 6 to the insideperiphery of the device 1. The connection tracks then descend to theinside of the device as far as the electronic circuit. The inventionmakes it possible to limit the influence of the connection tracks 7 sothat the detected capacitance is equivalent to a capacitance that wouldbe detected by an ideal electrode comprising only the useful part 6.

FIG. 6 shows a processing unit 5 controlling all of the components ofthe device 1. This processing unit can be a microprocessor or amicrocontroller equipped with conventional hardware and software meansfor controlling in particular the display screen and the matrix array ofelectrodes 3. This matrix array of electrodes is designed to detect thegestures of a finger 4 in a volume above the display screen 2.

The user moves his finger 4 which is detected by the processing unit 5by means of the matrix array of electrodes 3. The processing unitanalyses these gestures in order to feed an active software applicationwithin the device, in particular via the display screen 2.

In order that the detection of the finger 4 may be carried outefficiently without interference due to the connection tracks, theprocessing unit 5 is configured according to the invention for carryingout the operations such as described in FIG. 8.

The objective is to correct the capacitance values acquired in real timeoperating mode by the electrodes by using the virtual parametersobtained during the calibration.

The different operations used in operating mode by the method accordingto the invention as shown in FIG. 8 will now be described.

Acquisition

This is the measurement of the absolute capacitance of the matrix arrayof electrodes. The following are acquired: N voltages Vraw_(i)=k/C_(i),where C_(i) is the absolute capacitance measured on the actual electrodei, N is the number of electrodes and k is a gain chosen such thatmax(Vraw_(i))=Vmax Volt, a threshold chosen previously, for exampleVmax=5 V.

Filtering

This is a filtering operation, known as a max filter, such as describedin document FR1059203. In particular, two examples of such filteringare:

-   -   Vinf_raw(t0)=max {Vraw(t): t∈(−∞, t0)} or

Vinf_raw(t0)=max {Vraw(t): t∈(t0−windowsize, t0)}, where windowsize isthe time period of the auto-calibration window—i.e. the time period inwhich any unchanged interference will be considered as caused by anobject of no interest, t0 the time of the measurement.

Alternatively, the max filter can be replaced by a factory calibration,which provides the values of Vinf_raw, measured in the factory andstored in a memory area.

Correction A

The natural leakage capacitances of the electrodes are corrected.

The principle of the correction is to apply three successivetransformations:

-   -   a nonlinear function X1=F1(Vinf_raw) on the N voltages        individually, then    -   a linear transformation is applied on X1 using a matrix [M1] and        a translation vector known as an offset vector Y01:        Y1=[M1].X+Y01, Y01 being able to be zero; the matrix M1 and the        vector Y01 being obtained from the transformation model        determined during the calibration phase.

This transformation combines the actual values with each other throughthe matrix M1, then

-   -   the inverse nonlinear transformation Vinf_corr=F1⁻¹(Y1) is        applied.

Several functions F1 can be used, among which are:

-   -   F11(V):=1/V    -   F12(V):=1/(V/Vmax+β), where β>0, a number introduced to avoid        the singularity when V≈0, β=0.1, 0.2 or 0.3 can be chosen for        example.    -   F13(V):=V/Vmax.

Correction B

Here the absolute capacitances in the presence of the object of interestin the proximity of the actual electrodes are corrected.

The principle of the previous correction is used again but withdifferent input vectors. Three successive transformations are alsoapplied:

-   -   a nonlinear function X2=F2(Vraw, Vinf_raw) on the N voltages        individually, then    -   a linear transformation is applied on X2 using a matrix [M2] and        a translation vector known as an offset vector Y02:

Y2=[M2].X+Y02, it being possible for Y02 to be zero; the matrix M2 andthe vector Y02 being obtained from the transformation model determinedduring the calibration phase.

-   -   the inverse nonlinear transformation V_corr=F2⁻¹(Y2, Vinf_corr)        is applied. The function F2⁻¹ is inverse to the nonlinear        function F2 when the latter is considered as a function of its        first parameter F2(Vraw); the second parameter Vinf_raw being        considered as being fixed.

Several functions F2 can be used, among which are:

-   -   F21(V):=1/V    -   F22(V):=V/Vinf*    -   F23(V):=1−V/Vinf*    -   F24(V):=1/(V/Vmax+β)    -   F25(V):=V/Vmax.

For the calculations of the functions F22 and F23 and their inverses,Vinf*=Vinf_raw, for the direct nonlinear transformation, or Vinf_corrfor the nonlinear inverse transformation.

With the functions F21, F24 and F25, it is possible of carry out thecorrection B directly and obtain a vector V_corr of corrected actualvalues.

The matrices [M1] [M2] and the offset Y1 and Y2 are estimated by adigital regression method such as for example the PLS (Partial LeastSquare) method from the simulation of the device having actualelectrodes and a second device with virtual electrodes with a fingerpositioned on several locations over a volume above the device for theregression during the correction B (matrix M2 and vector Y2). In otherwords, M1,Y1 and M2,Y2 are different and are obtained by two PLSregressions:

-   -   (M1, Y1) on the inverse of the natural leakages of the        capacitances (1/Cinf), Cinf being the natural leakage        capacitance (in absence of the object of interest) of the        device.    -   (M2, Y2) on the absolute capacitances with the object that is        positioned in the detection volume.

Normalization

A max filter is used for calculating:

-   -   V_corr_f(t0)=max {Vcorr (t): t∈(−∞, t0)} or

V_corr_f(t0)=max {Vcorr (t): t∈(t0−windowsize, t0)}, where windowsize isthe time period of the auto-calibration window, i.e. the time period inwhich any unchanged interference will be considered as caused by anobject of no interest and t0 is the time of the measurement.

In the example described above, the nonlinear transformations A and Bbreak down into: (1) a nonlinear transformation independent of theinputs, followed by (2) an affine transformation, followed by (3)another independent nonlinear transformation. Another possibleembodiment of the transformations A and B is the artificial neuronenetworks model. The inputs V_raw and Vinf_raw and the outputs V_corr andVinf_corr will be used for the learning of the network.

Alternatively, the max filter can be replaced by a factory calibration,which provides the values of V_corr_f, measured in the factory andstored in a memory area.

Then an image known as the normalization image is produced to provide afinal image. This normalization depends on V_corr and V_corr_f. Forexample the image the values of which are the ratio of the two imagesV_corr and V_corr_f:

V_corr_nor=V_corr/V_corr_f

The normalized image thus calculated gives a function of the probabilitydensity of the presence of the object of interest. It is used by theprocessing unit in order to detect the presence and the position of theobject of interest (the finger) for example by calculating theexpectancy of the distribution by a barycentre method, or the MODE (thevalue most often adopted) of the probability density function. A“spline” type interpolation can be used for a sub-pixel resolution ofthe MODEs (MODE: the value of a random variable that has the greatestchance of occurring, it is the location of the maximum of theprobability density function).

In general, as seen in FIG. 9, the invention can comprise a calibrationphase during which a multi-variable nonlinear prediction model isdetermined by carrying out a nonlinear regression on the basis:

-   -   of actual values of absolute capacitance obtained for a        plurality of object positions with respect to the plane of        electrodes, and    -   of an image of probability densities obtained for a plurality of        object positions with respect to a plane of idealized        electrodes.

The invention then comprises a (routine) operation phase during which,at each detection, the model obtained in the calibration phase is used.In order to do this, after having carried out measurements on theelectrodes, a prediction is made by applying this nonlinearmulti-variable prediction model to the actual values of absolutecapacitance in order to obtain a probability densities image, theseprobability densities being considered as corrected absolute capacitancevalues that are used for the detection of the object.

Preferably, the calibration is carried out just once, the model beingsaved in the memory of each device.

Of course, the invention is not limited to the examples which have justbeen described and numerous adjustments can be made to these exampleswithout exceeding the scope of the invention.

1. An electronic device, comprising: a plurality of capacitiveelectrodes configured for detecting an object touching or hovering abovea surface of the electronic device, with one or more of the plurality ofcapacitive electrodes having an irregular electrode shape; a pluralityof connections configured for coupling the plurality of capacitiveelectrodes to one or more excitation and acquisition circuits; and aprocessing unit capable of retrieving a prediction model generated basedon a comparison of capacitance values, and applying the prediction modelto measured capacitance values obtained from one or more of theplurality of capacitive electrodes to correct the measured capacitancevalues.
 2. The electronic device of claim 1, wherein one or more of theplurality of connections are formed on a same layer as one or more ofthe plurality of capacitive electrodes.
 3. The electronic device ofclaim 1, wherein the one or more of the plurality of capacitiveelectrodes having an irregular shape comprise electrodes having anon-regular geometric shape.
 4. The electronic device of claim 1,wherein the prediction model is a multi-variable nonlinear predictionmodel.
 5. The electronic device of claim 4, wherein the multi-variablenonlinear prediction model is obtained by nonlinear regression.
 6. Theelectronic device of claim 1, the processing unit further capable of:obtaining an image of probability densities from the application of theprediction model to the measured capacitance values; using the image ofprobability densities as corrected measured capacitance values; andusing the corrected measured capacitance values to determine one or moreof a location and distance of the object from the surface of theelectronic device.
 7. A method for correcting measured capacitancevalues from a plurality of capacitive electrodes having an irregularelectrode shape, comprising: retrieving a prediction model generatedbased on a comparison of capacitance values, and applying the predictionmodel to the measured capacitance values obtained from one or more ofthe plurality of capacitive electrodes to correct the measuredcapacitance values.
 8. The method of claim 7, further comprisingobtaining the measured capacitance values from capacitive electrodescoupled to connecting traces formed on the same layer as the capacitiveelectrodes.
 9. The method of claim 7, further comprising obtaining themeasured capacitance values from capacitive electrodes having anon-regular geometric shape.
 10. The method of claim 7, wherein applyingthe prediction model comprises applying a multi-variable nonlinearprediction model.
 11. The method of claim 10, further comprisingobtaining the multi-variable nonlinear prediction model by nonlinearregression.
 12. The method of claim 7, further comprising: obtaining animage of probability densities from the application of the predictionmodel to the measured capacitance values; using the image of probabilitydensities as corrected measured capacitance values; and using thecorrected measured capacitance values to determine one or more of alocation and distance of the object from the surface of the electronicdevice.
 13. A non-transitory computer-readable storage medium storinginstructions for performing a method for correcting measured capacitancevalues from a plurality of capacitive electrodes having an irregularelectrode shape, the method comprising: retrieving a prediction modelgenerated based on a comparison of capacitance values, and applying theprediction model to the measured capacitance values obtained from one ormore of the plurality of capacitive electrodes to correct the measuredcapacitance values.
 14. The non-transitory computer-readable storagemedium of claim 13, the method further comprising obtaining the measuredcapacitance values from capacitive electrodes coupled to connectingtraces formed on the same layer as the capacitive electrodes.
 15. Thenon-transitory computer-readable storage medium of claim 13, the methodfurther comprising obtaining the measured capacitance values fromcapacitive electrodes having a non-regular geometric shape.
 16. Thenon-transitory computer-readable storage medium of claim 13, whereinapplying the prediction model comprises applying a multi-variablenonlinear prediction model.
 17. The non-transitory computer-readablestorage medium of claim 16, the method further comprising obtaining themulti-variable nonlinear prediction model by nonlinear regression. 18.The non-transitory computer-readable storage medium of claim 13, themethod further comprising: obtaining an image of probability densitiesfrom the application of the prediction model to the measured capacitancevalues; using the image of probability densities as corrected measuredcapacitance values; and using the corrected measured capacitance valuesto determine one or more of a location and distance of the object fromthe surface of the electronic device.
 19. The electronic device of claim1, wherein the prediction model is generated based on the comparison ofcapacitance values from actual and idealized electrodes for a pluralityof locations of the object.
 20. The method of claim 7, wherein theprediction model is generated based on the comparison of capacitancevalues from actual and idealized electrodes for a plurality of locationsof the object touching or hovering over a surface of an electronicdevice.
 21. The non-transitory computer-readable storage medium of claim13, wherein the prediction model is generated based on the comparison ofcapacitance values from actual and idealized electrodes for a pluralityof locations of the object touching or hovering over a surface of anelectronic device.